Talk · International Conference on Communications, Information, Electronic and Energy Systems · January 16, 2024
Quantitative Comparison of Machine Learning Clustering Methods for Tuberculosis Data Analysis
Marlen Kossakov, Assel Mukasheva, Гани Сергазин, Сырым Сейдазимов, Dinargul Mukhammejanova, Мадина Сыдыбаева
Abstract
In many fields, data-driven decision making has become essential due to machine learning (ML), which provides insights that improve productivity and quality of life. A basic machine learning approach called clustering helps find comparable data points. Clustering plays a critical role in the identification of patient subgroups and the customisation of treatment in the context of tuberculosis (TB) research. While prior studies have recognized its utility, a comprehensive comparative analysis of multiple clustering methods applied to TB data is lacking. Using TB data, this study thoroughly assesses and contrasts four well-known machine learning clustering algorithms: spectral clustering, DBSCAN, hierarchical clustering, and k-means. To evaluate the quality of a cluster, quantitative measures such as the silhouette score, Davies–Bouldin index, and Calinski–Harabasz index are utilised. The results provide quantitative insights that enhance comprehension of clustering and guide future research.
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